Update README.md

README.md

@@ -1,3 +1,64 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- image
+- segmentation
+- space
+pretty_name: 'SWiM: Spacecraft With Masks (Instance Segmentation)'
+size_categories:
+- 1K<n<1M
+task_categories:
+- image-segmentation
+task_ids:
+- instance-segmentation
+annotations_creators:
+- machine-generated
+- expert-generated
+---
+
+---
+
+# SWiM: Spacecraft With Masks
+
+A large-scale instance segmentation dataset of nearly 64k annotated spacecraft images that was created using real spacecraft models, superimposed on a mixture of real and synthetic backgrounds generated using NASA's TTALOS pipeline. To mimic camera distortions and noise in real-world image acquisition, we also added different types of noise and distortion to the images.
+
+## Dataset Summary
+The dataset contains over 64,000 annotated images with instance masks for varied spacecraft. It's structured for YOLO and segmentation applications, and chunked to stay within Hugging Face's per-folder file limits.
+
+
+## How to Use
+### Directory Structure Note
+
+Due to Hugging Face Hub's per-directory file limit (10,000 files), this dataset is chunked: each logical split (like `train/labels/`) is subdivided into folders (`000/`, `001/`, ...) containing no more than 5,000 files each.
+
+**Example Structure:**
+
+labels/
+  ├── 000/
+  │   ├── img_0.png
+  │   └── ...
+  ├── 001/
+  └── ...
+If you're using models/tools like **YOLO** or others that expect a **flat directory**, you may need to **merge these subfolders at load-time or during preprocessing**.
+
+
+## Code and Data Generation Pipeline
+
+All dataset generation scripts, preprocessing tools, and model training code are available on GitHub:
+
+[GitHub Repository: https://github.com/RiceD2KLab/SWiM](https://github.com/RiceD2KLab/SWiM)
+
+
+## Citation
+
+If you use this dataset, please cite:
+
+@misc{sam2025newdatasetperformancebenchmark,
+      title={A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers}, 
+      author={Jeffrey Joan Sam and Janhavi Sathe and Nikhil Chigali and Naman Gupta and Radhey Ruparel and Yicheng Jiang and Janmajay Singh and James W. Berck and Arko Barman},
+      year={2025},
+      eprint={2507.10775},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.10775}, 
+}